Current visual object recognition approaches use local descriptors to identify objects in an image. The local descriptors are usually extracted at specific locations in the image by interest point detectors. The interest point detectors find points in an image that can be characterized as having a clear, mathematically well-founded definition, a well-defined position in the image space, a rich local image structure, such that the point simplifies any further processing, and stability during deformations or illumination variations. Such characteristics are described in local descriptors. Objects can be recognized across images by matching these local descriptors.
However, many local descriptors often match common local structures in many different objects. This generates false matches when identifying objects in images. Some local descriptors never participate in any match. Such local descriptors impose computational and memory overhead for object recognition.